Instructions to use ProbeX/Model-J__SupViT__model_idx_0379 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__SupViT__model_idx_0379 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__SupViT__model_idx_0379") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0379") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__SupViT__model_idx_0379", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b596e82e8a79cff724964748619a005c3f06710a60e1a4042480cdc03d82c7a
- Size of remote file:
- 5.37 kB
- SHA256:
- 7780996bb93b4716018772f4c884ed24c76e74da8860ed5a558879c541b78576
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.